Brain-Computer Interfaces allow controlling machines through signals coming from Electroencephalography (EEG) analysis. Nowadays, there are several cheap electroencephalographs available on the market that guarantee good quality EEG signals. A very interesting approach in this area is related to detecting the emotional states of a user through the analysis of her EEG signal. In our study, we tried to detect the emotional polarity (Valence), the state of emotional excitement (Arousal), and the level of emotion control (Dominance). Through metric interpolation and Russell's circumplex model, it is possible to characterize and define the current emotional state of the user who wears the device. Our study presents a prototype of an EEG-based emotion recognizer that provides the user's emotional state exploitable as bio-feedback.

Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition / Ardito, C.; Bortone, I.; Colafiglio, T.; Di Noia, T.; Di Sciascio, E.; Lofù, D.; Narducci, F.; Sardone, R.; Sorino, P.. - 2022-:(2022), pp. 2689-2694. (Intervento presentato al convegno 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 tenutosi a cze nel 2022) [10.1109/SMC53654.2022.9945554].

Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition

Ardito C.;Bortone I.;Colafiglio T.;Di Noia T.;Di Sciascio E.;Lofù D.;Narducci F.;Sorino P.
2022-01-01

Abstract

Brain-Computer Interfaces allow controlling machines through signals coming from Electroencephalography (EEG) analysis. Nowadays, there are several cheap electroencephalographs available on the market that guarantee good quality EEG signals. A very interesting approach in this area is related to detecting the emotional states of a user through the analysis of her EEG signal. In our study, we tried to detect the emotional polarity (Valence), the state of emotional excitement (Arousal), and the level of emotion control (Dominance). Through metric interpolation and Russell's circumplex model, it is possible to characterize and define the current emotional state of the user who wears the device. Our study presents a prototype of an EEG-based emotion recognizer that provides the user's emotional state exploitable as bio-feedback.
2022
2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
978-1-6654-5258-8
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition / Ardito, C.; Bortone, I.; Colafiglio, T.; Di Noia, T.; Di Sciascio, E.; Lofù, D.; Narducci, F.; Sardone, R.; Sorino, P.. - 2022-:(2022), pp. 2689-2694. (Intervento presentato al convegno 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 tenutosi a cze nel 2022) [10.1109/SMC53654.2022.9945554].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/264361
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